引力搜索算法的实现

Question

我正在尝试实现引力搜索算法，但我无法获得与作者相同的结果。这是我的实现：

class GSA(MultiAgent):
def __init__(self, objf, population_size, dim, lb, ub, max_i, g0=100, alpha=20, eps=0.0001):
    super().__init__(objf=objf, population_size=population_size, dim=dim, ub=ub, lb=lb, max_i=max_i)
    self.g0 = g0
    self.alpha = alpha
    self.eps = eps
    self.v = np.zeros((self.population_size, self.dim))
    self.gworst = None
    self.run()

def update_gworst(self):
    idx = np.argmax(self.fitness)
    if self.gworst is None or self.fitness[idx] > self.gworst['fitness']:
        self.gworst = {'fitness': np.copy(self.fitness[idx]), 'position': np.copy(self.population[idx])}

def massCalculation(self):
    m = (self.fitness - self.gworst['fitness'])/(self.gbest['fitness'] - self.gworst['fitness']) + self.eps #equation 15
    m = m/np.sum(m) #equation 16
    return m

def g_field(self, m, i):
    g = self.g0 * np.exp(-self.alpha * i / self.max_i)  # equation 28
    subts = self.population - self.population[:, np.newaxis, :]
    r = np.sqrt(np.einsum('ijk,ijk->ij', subts, subts))
    m_ij = m * m[:, np.newaxis]
    rnd = np.random.uniform(size=(self.population_size, self.population_size, 1))
    e = g * (rnd * m_ij[:, :, np.newaxis] * subts) / (r[:, :, np.newaxis] + self.eps) #equation 7
    e = np.sum(e, 1)
    a = np.divide(e, m[:, np.newaxis]) #equation 10
    return a

def update_position(self, a):
    self.v = np.random.uniform(size=(self.population_size, self.dim)) * self.v + a #equation 11
    self.population = self.population + self.v #equation 12
    self.population = np.clip(self.population, self.lb, self.ub)

def run(self):
    i = 0
    while i < self.max_i:
        self.update_fitness() #get fitness of population
        self.update_gbest() #get gbest
        self.plot(i)
        #print(self.fitness), print('\n')
        print(self.gbest['fitness'])
        self.update_gworst()
        m = self.massCalculation()
        a = self.g_field(m, i)
        self.update_position(a)
        i += 1

问题出在哪里？谢谢您的帮助